Reinforcement Learning for Autonomous Driving in CARLA

CARLA is an open-source simulator for autonomous driving research.
In this article, we will introduce reinforcement learning for autonomous driving in CARLA.

By using reinforcement learning at CARLA, autonomous driving like the following video can be done.

CARLA

CARLA (Car Learning to Act) is an open-source simulator based on Unreal Engine 4 for autonomous driving research.

https://github.com/carla-simulator/carla

Reinforcement Learning for Autonomous Driving in CARLA

The following repository has codes and a trained model for executing the CoRL-2017 driving benchmark.

https://github.com/carla-simulator/reinforcement-learning

The training code is not included in this repository.

Setup CARLA Server

In the following article, you can use the CARLA server prepared on Ubuntu 16.04 LTS as it is.

Imitation Learning for Autonomous Driving in CARLA

Clone reinforcement-learning Repository

With the following command, clone the reinforcement-learning repository.

$ cd
$ git clone https://github.com/carla-simulator/reinforcement-learning

Setup Python Virtual Environment

Using Miniconda, create a virtual environment carla_rl of Python 3.6 with the following command.

$ conda create -n carla_rl python=3.6 chainer=1.24.0 cached-property=1.4.2 pillow=5.1.0 opencv=3.3.1 h5py=2.7.1

Execution of the CoRL-2017 Benchmark

In the first terminal, run the following command to start the CARLA server.

$ cd ~/carla-0.8.2
$ ./CarlaUE4.sh /Game/Maps/Town01 -carla-server -benchmark -fps=10 -windowed -ResX=640 -ResY=480

In another terminal, to start the Python client, execute the following command.

$ export PYTHONPATH=~/carla-0.8.2/PythonClient/:$PYTHONPATH
$ source activate carla_rl
$ cd ~/reinforcement-learning
$ python run_RL.py --corl-2017

Summary

In this article, we introduced reinforcement learning for autonomous driving in CARLA.